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What one of the following is the best practice to handle null pointer exception?

What one of the following is the best practice to handle null pointer exception?

Answer: Some of the best practices to avoid NullPointerException are: Use equals() and equalsIgnoreCase() method with String literal instead of using it on the unknown object that can be null. Use valueOf() instead of toString() ; and both return the same result.

How do you handle null values in Java 8?

We can get rid of all those null checks by utilizing the Java 8 Optional type. The method map accepts a lambda expression of type Function and automatically wraps each function result into an Optional . That enables us to pipe multiple map operations in a row. Null checks are automatically handled under the hood.

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How do you deal with null values?

Delete Rows with Missing Values: Missing values can be handled by deleting the rows or columns having null values. If columns have more than half of the rows as null then the entire column can be dropped. The rows which are having one or more columns values as null can also be dropped.

How do you assign a null value to an object in Java?

In Java, a null value can be assigned to an object reference of any type to indicate that it points to nothing. The compiler assigns null to any uninitialized static and instance members of reference type. In the absence of a constructor, the getArticles() and getName() methods will return a null reference.

How do you stop returning null in Java?

Another way to avoid returning null is to use a Null object design pattern. A null object is an object without behavior like a stub that a developer can return to the caller instead of returning null value. The caller doesn’t need to check the order for null value because a real but empty Order object is returned.

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How do you handle incomplete data?

Best techniques to handle missing data

  1. Use deletion methods to eliminate missing data. The deletion methods only work for certain datasets where participants have missing fields.
  2. Use regression analysis to systematically eliminate data.
  3. Data scientists can use data imputation techniques.